Are We On The Same Page? Hierarchical Explanation Generation for Planning Tasks in Human-Robot Teaming using Reinforcement Learning
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Providing explanations is considered an imperative ability for an AI agent in a human-robot teaming framework. The right explanation provides the rationale behind an AI agent's decision making. However, to maintain the human teammate's cognitive demand to comprehend the provided explanations, prior works have focused on providing explanations in a specific order or intertwining the explanation generation with plan execution. These approaches, however, do not consider the degree of details they share throughout the provided explanations. In this work, we argue that the explanations, especially the complex ones, should be abstracted to be aligned with the level of details the teammate desires to maintain the cognitive load of the recipient. The challenge here is to learn a hierarchical model of explanations and details the agent requires to yield the explanations as an objective. Moreover, the agent needs to follow a high-level plan in a task domain such that the agent can transfer learned teammate preferences to a scenario where lower-level control policies are different, while the high-level plan remains the same. Results confirmed our hypothesis that the process of understanding an explanation was a dynamic hierarchical process. The human preference that reflected this aspect corresponded exactly to creating and employing abstraction for knowledge assimilation hidden deeper in our cognitive process. We showed that hierarchical explanations achieved better task performance and behavior interpretability while reduced cognitive load. These results shed light on designing explainable agents utilizing reinforcement learning and planning across various domains.
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